Loss function-based evaluation of DSGE models

被引:221
|
作者
Schorfheide, F [1 ]
机构
[1] Univ Penn, Dept Econ, Philadelphia, PA 19104 USA
关键词
D O I
10.1002/jae.582
中图分类号
F [经济];
学科分类号
02 ;
摘要
In this paper we propose a Bayesian econometric procedure for the evaluation and comparison of DSGE models. Unlike in many previous econometric approaches we explicitly take into account the possibility that the DSGE models are misspecified and introduce a reference model to complete the model space. Three loss functions are proposed to assess the discrepancy between DSGE model predictions and an overall posterior distribution of population characteristics that the researcher is trying to match. The evaluation procedure is applied to the comparison of a standard cash-in-advance (CIA) and a portfolio adjustment cost (PAC) model. We find that the CIA model has higher posterior probability than the PAC model and achieves a better in-sample time series fit. Both models overpredict the magnitude of the negative correlation between output growth and inflation. However, unlike the PAC model, the CIA model is not able to generate a positive real effect of money growth shocks on aggregate output. Overall, the impulse response dynamics of the PAC model resemble the posterior mean impulse response functions more closely than the responses of the CIA model. Copyright (C) 2000 John Wiley & Sons, Ltd.
引用
收藏
页码:645 / 670
页数:26
相关论文
共 50 条
  • [1] Loss function-based evaluation of physician report cards
    de la Guardia F.H.
    Hwang J.
    Adams J.L.
    Paddock S.M.
    [J]. Health Services and Outcomes Research Methodology, 2018, 18 (2) : 96 - 108
  • [2] Generalized ε-Loss Function-Based Regression
    Anand, Pritam
    Khemchandani), Reshma Rastogi (nee
    Chandra, Suresh
    [J]. MACHINE INTELLIGENCE AND SIGNAL ANALYSIS, 2019, 748 : 395 - 409
  • [3] A new loss function-based method for multiresponse optimization
    Ko, YH
    Kim, KJ
    Jun, CH
    [J]. JOURNAL OF QUALITY TECHNOLOGY, 2005, 37 (01) : 50 - 59
  • [4] Effects of Function-Based Models in Biologically Inspired Design
    Liu, Wei
    Rosa, Francesco
    Cascini, Gaetano
    Tan, Runhua
    [J]. JOURNAL OF INTEGRATED DESIGN & PROCESS SCIENCE, 2020, 24 (01) : 85 - 108
  • [5] A Note on Adaptive Function-Based Models: The Case of Mobility
    Drut, Marion
    [J]. JOURNAL OF ECONOMIC ISSUES, 2015, 49 (04) : 1124 - 1133
  • [6] Loss function-based change point detection in risk measures
    Lazar, Emese
    Wang, Shixuan
    Xue, Xiaohan
    [J]. EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2023, 310 (01) : 415 - 431
  • [7] Evaluation of client preference for function-based treatment packages
    Hanley, GP
    Piazza, CC
    Fisher, WW
    Contrucci, SA
    Maglieri, KA
    [J]. JOURNAL OF APPLIED BEHAVIOR ANALYSIS, 1997, 30 (03) : 459 - 473
  • [8] A function-based computational method for design concept evaluation
    Hao, Jia
    Zhao, Qiangfu
    Yan, Yan
    [J]. ADVANCED ENGINEERING INFORMATICS, 2017, 32 : 237 - 247
  • [9] Implicit function-based phantoms for evaluation of registration algorithms
    Gopalakrishnan, G
    Poston, T
    Nagaraj, N
    Mullick, R
    Knoplioch, J
    [J]. Medical Imaging 2005: Image Processing, Pt 1-3, 2005, 5747 : 1310 - 1316
  • [10] Frequentist Evaluation of Small DSGE Models
    Bardsen, Gunnar
    Fanelli, Luca
    [J]. JOURNAL OF BUSINESS & ECONOMIC STATISTICS, 2015, 33 (03) : 307 - 322